Healthcare supply chain management: Towards 3D MRI aorta model with CPU-GPU parallel architecture for medical manufacture

Figures & data

Figure 1. Supply chain for Healthcare management

Figure 2. Supply chain for MRI tools

Figure 3. Programming model

Figure 4. Architecture of OpenFOAM

Table 1. Solvers with OpenFOAM

“Basic” CFD codes (Materano et al., 2021),(Benchikh Le Hocine et al., 2021),(Blanco-Aguilera et al., 2020)
laplacianFoam	Laplace equation solver for a scalar quantity
overLaplacianDyMFoam	Laplace equation solver for a scalar quantity
potentialFoam	Potential flow solver which solves for the velocity potential, to calculate the flux-field, from which the velocity field is obtained by reconstructing the flux
overPotentialFoam	Potential flow solver which solves for the velocity potential, to calculate the flux-field, from which the velocity field is obtained by reconstructing the flux
scalarTransportFoam	Passive scalar transport equation solver
Incompressible flow (Tsiolakis et al., 2020),(Deng et al., 2017),(Robertson et al., 2015)
adjointOptimisationFoam	An automated adjoint-based optimisation loop. Supports multiple types of optimisation (shape, topology etc)
adjointShapeOptimizationFoam	Steady-state solver for incompressible, turbulent flow of non-Newtonian fluids with optimisation of duct shape by applying ”blockage” in regions causing pressure loss as estimated using an adjoint formulation
boundaryFoam	Steady-state solver for incompressible, 1D turbulent flow, typically to generate boundary layer conditions at an inlet
icoFoam	Transient solver for incompressible, laminar flow of Newtonian fluids
nonNewtonianIcoFoam	Transient solver for incompressible, laminar flow of non-Newtonian fluids
Compressible flow(Modesti & Pirozzoli, 2017),(C. Wang et al., 2020),(Ye et al., 2019)
rhoCentralFoam	Density-based compressible flow solver based on central-upwind schemes of Kurganov and Tadmor
rhoCentralDyMFoam	Density-based compressible flow solver based on central-upwind schemes of Kurganov and Tadmor with support for mesh-motion and topology changes
rhoPimpleAdiabaticFoam	Transient solver for laminar or turbulent flow of weakly compressible fluids for low Mach number aeroacoustic applications
rhoPimpleFoam	Transient solver for turbulent flow of compressible fluids for HVAC and similar applications, with optional mesh motion and mesh topology changes

Figure 5. (a) original TRIKS cardiac sequences acquired with MRI;(bsteps of reconstruction and segmentation of the 3D aorta structure); (c) 3D reconstruction of the descending aorta

Table 2. Parallel processing of the mesh of the aorta

		Time to trace the mesh (s)	Time for decomposition of the mesh (s)	Time for finalizing the mesh (s)	Very worse	worse	moderate	fine	very fine
Parallel processing: number of cores	1	2,43	36,26	76,78	X
	2	1,17	17,31	35,3
	4	0,72	9,72	21,29
	8	0,43	5,53	15,18
	16	0,27	3,94	12,2
Parallel processing: number of cores	1	2,55	44,24	94,2		X
	2	1,18	19,52	41,67
	4	0,65	10,82	25,98
	8	0,48	6,96	17,77
	16	0,33	4,59	14,35
Parallel processing: number of cores	1	16,1	194,14	422,03			X
	2	7,53	88,78	175,96
	4	4,04	47,73	102,1
	8	2,21	25,17	66,46
	16	1,41	16,29	47,57
Parallel processing: number of cores	1	Mesh failure (not enough processors)						X
	2
	4	19,21	205,76	455,56
	8	10,55	118,21	229,96
	16	6,72	77,28	207,74
Parallel processing: number of cores	1	Mesh failure (not enough processors)							X
	2
	4	Problem of memory space
	8	30,21	547,58	1310,28
	16	17,75	341,92	976,82

Figure 6. 3D Mesh of the aorta

Figure 7. Computation time done on the CPU host

Figure 8. Computation time for GPU-NVIDIA

Figure 9. Comparison between CPU and GPU performance

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